Entrepreneurial Complexity: Methods and Applications deals with theoretical and practical results of Entrepreneurial Sciences and Management (ESM), emphasising qualitative and quantitative methods. ESM has been a modern and exciting research field in which methods from various disciplines have been applied. However, the existing body of literature lacks the proper use of mathematical and formal models; individuals who perform research in this broad interdisciplinary area have been trained differently. In particular, they are not used to solving business-oriented problems mathematically. This book utilises formal techniques in ESM as an advantage for developing theories and models which are falsifiable.
- Discusses methods for defining and measuring complexity in entrepreneurial sciences
- Summarises new technologies and innovation-based techniques in entrepreneurial sciences
- Outlines new formal methods and complexity-models for entrepreneurship
- To date no book has been dedicated exclusively to use formal models in Entrepreneurial Sciences and Management
Table of Contents
1 Entrepreneurs for Renewables: Emergence of Innovation and
Entrepreneurship in Complex Social Systems 1
Diana Süsser, Barbara Weig, Martin Döring and
Beate M.W. Ratter
2 Entrepreneurial Network Effects: Empirical Observations of
Entrepreneurial Networks in a World of Complexity 49
John T. Scott
3 Entrepreneurial Process: The Overbearing Role of Complex Social
Adekiya Adewale and Ahmed Musbah Aboyssir
4 Sustainable Entrepreneurial Activity within Complex Economic Systems 89
Panagiotis E. Petrakis and Kyriaki I. Kafka
5 Integration Opportunities of Stability-Oriented Processes for Real Estate
Transaction Entities 109
Linda Kauškale and Ineta Geipele
6 Entrepreneurial Dispositions Personality Inventory: Development and
Konrad Janowski, Marcin Waldemar Staniewski and
7 Mapping the Entrepreneurship from a Gender Perspective 141
Magdalena Suárez-Ortega, María del Rocío
Gálvez-García and María Fe Sánchez-García
Matthias Dehmer is a professor at the University of Applied Sciences Upper Austria, Steyr School of Management and UMIT – The Health and Life Sciences University in Austria. He also holds a guest professorship at Nankai University, College of Artificial Intelligence in China. His research interests are in graph theory, complex networks, complexity, data science, machine learning, big data analytics, and information theory. In particular, he is also working on machine learningbased methods to design new data analysis methods for solving problems in manufacturing and production.
Frank Emmert-Streib is a professor at Tampere University, Finland, heading the Predictive Society and Data Analytics Lab. His research interests are in the field of data science, machine learning and network science in the development and application of methods from statistics and machine learning for the analysis of big data from genomics, finance, social media and business.
Herbert Jodlbauer is a professor at the University of Applied Sciences Upper Austria, Steyr School of Management and also acts as a director of studies of the bachelor study program Production and Management and the master study program Operations Management. Furthermore, he leads the trans-faculty institute of Smart Production. His research is primarily concerned with production planning, time continuous production models, financial valuation of production related decisionmaking as well as digitalization.